Báo cáo khoa học: "Correcting errors in speech recognition with articulatory dynamics" pot

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 60–68,
Uppsala, Sweden, 11-16 July 2010.
c
2010 Association for Computational Linguistics
Correcting errors in speech recognition with articulatory dynamics
Frank Rudzicz
University of Toronto, Department of Computer Science
Toronto, Ontario, Canada

Abstract
We introduce a novel mechanism for
incorporating articulatory dynamics into
speech recognition with the theory of task
dynamics. This system reranks sentence-
level hypotheses by the likelihoods of
their hypothetical articulatory realizations
which are derived from relationships
learned with aligned acoustic/articulatory
data. Experiments compare this with two
baseline systems, namely an acoustic hid-
den Markov model and a dynamic Bayes
network augmented with discretized rep-
resentations of the vocal tract. Our sys-
tem based on task dynamics reduces word-
error rates significantly by 10.2% relative
to the best baseline models.
1 Introduction
Although modern automatic speech recognition
(ASR) takes several cues from the biological per-
ception of speech, it rarely models its biological
production. The result is that speech is treated

as a surface acoustic phenomenon with lexical or
phonetic hidden dynamics but without any phys-
ical constraints in between. This omission leads
to some untenable assumptions. For example,
speech is often treated out of convenience as a se-
quence of discrete, non-overlapping packets, such
as phonemes, despite the fact that some major dif-
ficulties in ASR, such as co-articulation, are by
definition the result of concurrent physiological
phenomena (Hardcastle and Hewlett, 1999).
Many acoustic ambiguities can be resolved
with knowledge of the vocal tract’s configuration
(O’Shaughnessy, 2000). For example, the three
nasal sonorants, /m/, /n/, and /ng/, are acousti-
cally similar (i.e., they have large concentrations
of energy at the same frequencies) but uniquely
and reliably involve bilabial closure, tongue-tip
elevation, and tongue-dorsum elevation, respec-
tively. Having access to the articulatory goals of
the speaker would, in theory, make the identifica-
tion of linguistic intent almost trivial. Although
we don’t typically have access to the vocal tract
during speech recognition, its configuration can
be estimated reasonably well from acoustics alone
within adequate models or measurements of the
vocal tract (Richmond et al., 2003; Toda et al.,
2008). Evidence that such inversion takes place
naturally in humans during speech perception sug-
gests that the discriminability of speech sounds de-
pends powerfully on their production (Liberman

and Mattingly, 1985; D’Ausilio et al., 2009).
This paper describes the use of explicit models
of physical speech production within recognition
systems. Initially, we augment traditional models
of ASR with probabilistic relationships between
acoustics and articulation learned from appropri-
ate data. This leads to the incorporation of a high-
level, goal-oriented, and control-based theory of
speech production within a novel ASR system.
2 Background and related work
The use of theoretical (phonological) features of
the vocal tract has provided some improvement
over traditional acoustic ASR systems in phoneme
recognition with neural networks (Kirchhoff,
1999; Roweis, 1999), but there has been very
little work in ASR informed by direct measure-
ments of the vocal tract. Recently, Markov et
al. (2006) have augmented hidden Markov models
with Bayes networks trained to describe articula-
tory constraints from a small amount of Japanese
vocal tract data, resulting in a small phoneme-
error reduction. This work has since been ex-
panded upon to inform ASR systems sensitive to
physiological speech disorders (Rudzicz, 2009).
Common among previous efforts is an interpre-
tation of speech as a sequence of short, instanta-
neous observations devoid of long-term dynamics.
60
2.1 Articulatory phonology
Articulatory phonology bridges the divide be-

tween the physical manifestation of speech and its
underlying lexical intentions. Within this disci-
pline, the theory of task dynamics is a combined
model of physical articulator motion and the plan-
ning of abstract vocal tract configurations (Saltz-
man, 1986). This theory introduces the notion that
all observed patterns of speech are the result of
overlapping gestures, which are abstracted goal-
oriented reconfigurations of the vocal tract, such
as bilabial closure or velar opening (Saltzman and
Munhall, 1989). Each gesture occurs within one
of the following tract variables (TVs): velar open-
ing (VEL), lip aperture (LA) and protrusion (LP),
tongue tip constriction location (TTCL) and de-
gree (TTCD)
1
, tongue body constriction location
(TBCL) and degree (TBCD), lower tooth height
(LTH), and glottal vibration (GLO). For example,
the syllable pub consists of an onset (/p/), a nu-
cleus (/ah/), and a coda (/b/). Four gestural goals
are associated with the onset, namely the shutting
of GLO and of VEL, and the closure and release of
LA. Similarly, the nucleus of the syllable consists
of three goals, namely the relocation of TBCD and
TBCL, and the opening of GLO. The presence and
extent of these gestural goals are represented by
filled rectangles in figure 1. Inter-gestural timings
between these goals are specified relative to one
another according to human data as described by

Nam and Saltzman (2003).
TBCD
closed
open
GLO
open
closed
LA
open
closed
100 200 300 400
Time (ms)
Figure 1: Canonical example pub from Saltzman
and Munhall (1989).
The presence of these discrete goals influences
the vocal tract dynamically and continuously
as modelled by the following non-homogeneous
second-order linear differential equation:
Mz

+ Bz

+ K(z − z
∗
) = 0. (1)
1
Constriction locations generally refer to the front-back
dimension of the vocal tract and constriction degrees gener-
ally refer to the top-down dimension.
Here, z is a continuous vector representing the in-

stantaneous positions of the nine tract variables,
z
∗
is the target (equilibrium) positions of those
variables, and vectors z

and z

represent the first
and second derivatives of z with respect to time
(i.e., velocity and acceleration), respectively. The
matrices M, B, and K are syllable-specific coef-
ficients describing the inertia, damping, and stiff-
ness, respectively, of the virtual gestures. Gener-
ally, this theory assumes that the tract variables are
mutually independent, and that the system is criti-
cally damped (i.e., the tract variables do not oscil-
late around their equilibrium positions) (Nam and
Saltzman, 2003). The continuous state, z, of equa-
tion (1) is exemplified by black curves in figure 1.
2.2 Articulatory data
Tract variables provide the dimensions of an ab-
stract gestural space independent of the physical
characteristics of the speaker. In order to com-
plete our articulatory model, however, we require
physical data from which to infer these high-level
articulatory goals.
Electromagnetic articulography (EMA) is a
method to measure the motion of the vocal tract
during speech. In EMA, the speaker is placed

within a low-amplitude electromagnetic field pro-
duced within a cube of a known geometry. Tiny
sensors within this field induce small electric cur-
rents whose energy allows the inference of artic-
ulator positions and velocities to within 1 mm of
error (Yunusova et al., 2009). We derive data for
the following study from two EMA sources:
• The University of Edinburgh’s MOCHA
database, which provides phonetically-
balanced sentences repeated from TIMIT
(Zue et al., 1989) uttered by a male and a
female speaker (Wrench, 1999), and
• The University of Toronto’s TORGO
database, from which we select sentences
repeated from TIMIT from two females
and three males (Rudzicz et al., 2008).
(Cerebrally palsied speech, which is the
focus of this database, is not included here).
For the following study we use the eight 2D po-
sitions common to both databases, namely the up-
per lip (UL), lower lip (LL), upper incisor (UI),
lower incisor (LI), tongue tip (TT), tongue blade
(TB), and tongue dorsum (TD). Since these po-
sitions are recorded in 3D in TORGO, we project
61
these onto the midsagittal plane. (Additionally, the
MOCHA database provides velum (V) data on this
plane, and TORGO provides the left and right lip
corners (LL and RL) but these are excluded from
study except where noted).

All articulatory data is aligned with its associ-
ated acoustic data, which is transformed to Mel-
frequency cepstral coefficients (MFCCs). Since
the 2D EMA system in MOCHA and the 3D EMA
system in TORGO differ in their recording rates,
the length of each MFCC frame in each database
must differ in order to properly align acoustics
with articulation in time. Therefore, each MFCC
frame covers 16 ms in the TORGO database, and
32 ms in MOCHA. Phoneme boundaries are de-
termined automatically in the MOCHA database
by forced alignment, and by a speech-language
pathologist in the TORGO database.
We approximate the tract variable space from
the physical space of the articulators, in general,
through principal component analysis (PCA) on
the latter, and subsequent sigmoid normalization
on [0, 1]. For example, the LTH tract variable is in-
ferred by calculating the first principal component
of the two-dimensional lower incisor (LI) motion
in the midsagittal plane, and by normalizing the
resulting univariate data through a scaled sigmoid.
The VEL variable is inferred similarly from velum
(V) EMA data. Tongue tip constriction location
and degree (TTCL and TTCD, respectively) are
inferred from the 1
st
and 2
nd
principal components

of tongue tip (TT) EMA data, with TBCL and
TBCD inferred similarly from tongue body (TB)
data. Finally, the glottis (GLO) is inferred by voic-
ing detection on acoustic energy below 150 Hz
(O’Shaughnessy, 2000), lip aperture (LA) is the
normalized Euclidean distance between the lips,
and lip protrusion (LP) is the normalized 2
nd
prin-
cipal component of the midpoint between the lips.
All PCA is performed without segmentation of the
data. The result is a low-dimensional set of contin-
uous curves describing goal-relevant articulatory
variables. Figure 2, for example, shows the degree
of the lip aperture (LA) over time for all instances
of the /b/ phoneme in the MOCHA database. The
relevant articulatory goal of lip closure is evident.
3 Baseline systems
We now turn to the task of speech recognition.
Traditional Bayesian learning is restricted to uni-
versal or immutable relationships, and is agnos-
0 50 100 150 200
0
0.2
0.4
0.6
0.8
1
Time (ms)
normalized LA

Figure 2: Lip aperture (LA) over time during all
MOCHA instances of /b/.
tic towards dynamic systems or time-varying rela-
tionships. Dynamic Bayes networks (DBNs) are
directed acyclic graphs that generalize the power-
ful stochastic mechanisms of Bayesian represen-
tation to temporal sequences. We are free to ex-
plicitly provide topological (i.e., dependency) re-
lationships between relevant variables in our mod-
els, which can include measurements of tract data.
We examine two baseline systems. The
first is the standard acoustic hidden Markov
model (HMM) augmented with a bigram language
model, as shown in figure 3(a). Here, W
t
→ W
t+1
represents word transition probabilities, learned
by maximum likelihood estimation, and Ph
t
→
Ph
t+1
represents phoneme transition probabilities
whose order is explicitly specified by the relation-
ship W
t
→ Ph
t
. Likewise, each phoneme Ph con-

ditions the sub-phoneme state, Q
t
, whose transi-
tion probabilities Q
t
→ Q
t+1
describe the dynam-
ics within phonemes. The variable M
t
refers to
hidden Gaussian indices so that the likelihoods
of acoustic observations, O
t
, are represented by a
mixture of 4, 8, 16, or 32 Gaussians for each state
and each phoneme. See Murphy (2002) for a fur-
ther description of this representation.
The second baseline model is the articulatory
dynamic Bayes network (DBN-A). This augments
the standard acoustic HMM by replacing hidden
indices, M
t
, with discrete observations of the vo-
cal tract, K
t
, as shown in figure 3(b). The pattern
of acoustics within each phoneme is dependent on
a relatively restricted set of possible articulatory
configurations (Roweis, 1999). To find these dis-

crete positions, we obtain k vectors that best de-
62
scribe the articulatory data according to k -means
clustering with the sum-of-squares error function.
During training, the DBN variable K
t
is set ex-
plicitly to the index of the mean vector nearest to
the current frame of EMA data at time t. In this
way, the relationship K
t
→ O
t
allows us to learn
how discretized articulatory configurations affect
acoustics. The training of DBNs involves a spe-
cialized version of expectation-maximization, as
described in the literature (Murphy, 2002; Ghahra-
mani, 1998). During inference, variables W
t
, Ph
t
,
and K
t
become hidden and we marginalize over
their possible values when computing their likeli-
hoods. Bigrams are computed by maximum like-
lihood on lexical annotations in the training data.
M

t
O
t
M
t+1
O
t+1
Q
t
Ph
t
Q
t+1
Ph
t+1
W
t
W
t+1
(a) HMM
K
t
O
t
K
t+1
O
t+1
Q
t

Ph
t
Q
t+1
Ph
t+1
W
t
W
t+1
(b) DBN-A
Figure 3: Baseline systems: (a) acoustic hidden
Markov model and (b) articulatory dynamic Bayes
network. Node W
t
represents the current word, Ph
t
is the current phoneme, Q
t
is that phoneme’s dy-
namic state, O
t
is the acoustic observation, M
t
is
the Gaussian mixture component, and K
t
is the dis-
cretized articulatory configuration. Filled nodes
represent observed variables during training, al-

though only O
t
is observed during recognition.
Square nodes are discrete variables while circular
nodes are continuous variables.
4 Switching Kalman filter
Our first experimental system attempts speech
recognition given only articulatory data. The true
state of the tract variables at time t − 1 constitutes
a 9-dimensional vector, x
t−1
, of continuous val-
ues. Under the task dynamics model of section
2.1, the motions of these tract variables obey crit-
ically damped second-order oscillatory relation-
ships. We start with the simplifying assumption of
linear dynamics here with allowances for random
Gaussian process noise, v
t
, since articulatory be-
haviour is non-deterministic. Moreover, we know
that EMA recordings are subject to some error
(usually less than 1 mm (Yunusova et al., 2009)),
so the actual observation at time t, y
t
, will not in
general be the true position of the articulators. As-
suming that the relationship between y
t
and x

t
is
also linear, and that the measurement noise, w
t
,
is also Gaussian, then the dynamical articulatory
system can be described by
x
t
= D
t
x
t−1
+ v
t
y
t
= C
t
x
t
+ w
t
.
(2)
Eqs. 2 form the basis of the Kalman filter
which allows us to use EMA measurements di-
rectly, rather than quantized abstractions thereof
as in the DBN-A model. Obviously, since artic-
ulatory dynamics vary significantly for different

goals, we replicate eq. (2) for each phoneme and
connect these continuous Kalman filters together
with discrete conditioning variables for phoneme
and word, resulting in the switching Kalman fil-
ter (SKF) model. Here, parameters D
t
and v
t
are
implicit in the relationship x
t
→ x
t+1
, and param-
eters C
t
and w
t
are implicit in x
t
→ y
t
. In this
model, observation y
t
is the instantaneous mea-
surements derived from EMA, and x
t
is their true
hidden states. These parameters are trained using

expectation-maximization, as described in the lit-
erature (Murphy, 1998; Deng et al., 2005).
5 Recognition with task dynamics
Our goal is to integrate task dynamics within an
ASR system for continuous sentences called TD-
ASR. Our approach is to re-rank an N-best list of
sentence hypotheses according to a weighted like-
lihood of their articulatory realizations. For ex-
ample, if a word sequence W
i
: w
i,1
w
i,2
w
i,m
has likelihoods L
X
(W
i
) and L
Λ
(W
i
) according to
purely acoustic and articulatory interpretations of
an utterance, respectively, then its overall score
would be
L(W
i

) = αL
X
(W
i
) + (1 −α)L
Λ
(W
i
) (3)
given a weighting parameter α set manually, as in
section 6.2. Acoustic likelihoods L
X
(W
i
) are ob-
tained from Viterbi paths through relevant HMMs
in the standard fashion.
5.1 The TADA component
In order to obtain articulatory likelihoods, L
Λ
(W
i
),
for each word sequence, we first generate artic-
ulatory realizations of those sequences according
63
to task dynamics. To this end, we use compo-
nents from the open-source TADA system (Nam
and Goldstein, 2006), which is a complete imple-
mentation of task dynamics. From this toolbox,

we use the following components:
• A syllabic dictionary supplemented with
the International Speech Lexicon Dictionary
(Hasegawa-Johnson and Fleck, 2007). This
breaks word sequences W
i
into syllable se-
quences S
i
consisting of onsets, nuclei, and
coda and covers all of MOCHA and TORGO.
• A syllable-to-gesture lookup table. Given
a syllabic sequence, S
i
, this table provides
the gestural goals necessary to produce those
syllables. For example, given the syllable
pub in figure 1, this table provides the tar-
gets for the GLO, VEL, TBCL, and TBCD
tract variables, and the parameters for the
second-order differential equation, eq. 1,
that achieves those goals. These parameters
have been empirically tuned by the authors
of TADA according to a generic, speaker-
independent representation of the vocal tract
(Saltzman and Munhall, 1989).
• A component that produces the continuous
tract variable paths that produce an utter-
ance. This component takes into account var-
ious physiological aspects of human speech

production, including intergestural and in-
terarticulator co-ordination and timing (Nam
and Saltzman, 2003; Goldstein and Fowler,
2003), and the neutral (“schwa”) forces of the
vocal tract (Saltzman and Munhall, 1989).
This component takes a sequence of gestu-
ral goals predicted by the segment-to-gesture
lookup table, and produces appropriate paths
for each tract variable.
The result of the TADA component is a set of
N 9-dimensional articulatory paths, TV
i
, neces-
sary to produce the associated word sequences, W
i
for i = 1 N. Since task dynamics is a prescrip-
tive model and fully deterministic, TV
i
sequences
are the canonical or default articulatory realiza-
tions of the associated sentences. These canonical
realizations are independent of our training data,
so we transform them in order to more closely re-
semble the observed articulatory behaviour in our
EMA data. Towards this end, we train a switch-
ing Kalman filter identical to that in section 4, ex-
cept the hidden state variable x
t
is replaced by the
observed instantaneous canonical TVs predicted

by TADA. In this way we are explicitly learning
a relationship between TADA’s task dynamics and
human data. Since the lengths of these sequences
are generally unequal, we align the articulatory be-
haviour predicted by TADA with training data from
MOCHA and TORGO using standard dynamic
time warping (Sakoe and Chiba, 1978). During
run-time, the articulatory sequence y
t
most likely
to have been produced by the human data given the
canonical sequence TV
i
is inferred by the Viterbi
algorithm through the SKF model with all other
variables hidden. The result is a set of articulatory
sequences, TV
∗
i
, for i = 1 N, that represent the
predictions of task dynamics that better resemble
our data.
5.2 Acoustic-articulatory inversion
In order to estimate the articulatory likelihood
of an utterance, we need to evaluate each trans-
formed articulatory sequence, TV
∗
i
, within proba-
bility distributions ranging over all tract variables.

These distributions can be inferred using acoustic-
articulatory inversion. There are a number of ap-
proaches to this task, including vector quantiza-
tion, and expectation-maximization with Gaussian
mixtures (Hogden and Valdez, 2001; Toda et al.,
2008). These approaches accurately inferred the
xy position of articulators to within 0.41 mm and
2.73 mm. Here, we modify the approach taken
by Richmond et al. (2003), who estimate proba-
bility functions over the 2D midsagittal positions
of 7 articulators, given acoustics, with a mixture-
density network (MDN). An MDN is essentially a
typical discriminative multi-layer neural network
whose output consists of the parameters to Gaus-
sian mixtures. Here, each Gaussian mixture de-
scribes a probability function over TV positions
given the acoustic frame at time t. For exam-
ple, figure 4 shows an intensity map of the likely
values for tongue-tip constriction degree (TTCD)
for each frame of acoustics, superimposed with
the ‘true’ trajectory of that TV. Our networks are
trained with acoustic and EMA-derived data as de-
scribed in section 2.2.
5.3 Recognition by reranking
During recognition of a test utterance, a standard
acoustic HMM produces word sequence hypothe-
ses, W
i
, and associated likelihoods, L(W
i

), for i =
1 N. The expected canonical motion of the tract
variables, TV
i
is then produced by task dynamics
64
Figure 4: Example probability density of tongue
tip constriction degree over time, inferred from
acoustics. The true trajectory is superimposed as a
black curve.
for each of these word sequences and transformed
by an SKF to better match speaker data, giving
TV
∗
i
. The likelihoods of these paths are then eval-
uated within probability distributions produced by
an MDN. The mechanism for producing the artic-
ulatory likelihood is shown in figure 5. The overall
likelihood, L(W
i
) = αL
X
(W
i
) + (1 − α)L
Λ
(W
i
), is

then used to produce a final hypothesis list for the
given acoustic input.
6 Experiments
Experimental data is obtained from two sources,
as described in section 2.2. We procure 1200
sentences from Toronto’s TORGO database, and
896 from Edinburgh’s MOCHA. In total, there are
460 total unique sentence forms, 1092 total unique
word forms, and 11065 total words uttered. Ex-
cept where noted, all experiments randomly split
the data into 90% training and 10% testing sets for
5-cross validation. MOCHA and TORGO data are
never combined in a single training set due to dif-
fering EMA recording rates. In all cases, models
are database-dependent (i.e., all TORGO data is
conflated, as is all of MOCHA).
For each of our baseline systems, we calcu-
late the phoneme-error-rate (PER) and word-error-
rate (WER) after training. The phoneme-error-
rate is calculated according to the proportion of
frames of speech incorrectly assigned to the proper
phoneme. The word-error-rate is calculated as
the sum of insertion, deletion, and substitution er-
rors in the highest-ranked hypothesis divided by
the total number of words in the correct orthogra-
phy. The traditional HMM is compared by vary-
ing the number of Gaussians used in the modelling
System Parameters PER (%) WER (%)
HMM
|M| = 4 29.3 14.5

|M| = 8 27.0 13.9
|M| = 16 26.1 10.2
|M| = 32 25.6 9.7
DBN-A
|K| = 4 26.1 13.0
|K| = 8 25.2 11.3
|K| = 16 24.9 9.8
|K| = 32 24.8 9.4
Table 1: Phoneme- and Word-Error-Rate (PER
and WER) for different parameterizations of the
baseline systems.
No. of Gaussians
1 2 3 4
LTH −0.28 −0.18 −0.15 −0.11
LA −0.36 −0.32 −0.30 −0.29
LP −0.46 −0.44 −0.43 −0.43
GLO −1.48 −1.30 −1.29 −1.25
TTCD −1.79 −1.60 −1.51 −1.47
TTCL −1.81 −1.62 −1.53 −1.49
TBCD −0.88 −0.79 −0.75 −0.72
TDCL −0.22 −0.20 −0.18 −0.17
Table 2: Average log likelihood of true tract vari-
able positions in test data, under distributions pro-
duced by mixture density networks with varying
numbers of Gaussians.
of acoustic observations. Similarly, the DBN-A
model is compared by varying the number of dis-
crete quantizations of articulatory configurations,
as described in section 3. Results are obtained by
direct decoding. The average results across both

databases, between which there are no significant
differences, are shown in table 1. In all cases
the DBN-A model outperforms the HMM, which
highlights the benefit of explicitly conditioning
acoustic observations on articulatory causes.
6.1 Efficacy of TD-ASR components
In order to evaluate the whole system, we start by
evaluating its parts. First, we test how accurately
the mixture-density network (MDN) estimates the
position of the articulators given only information
from the acoustics available during recognition.
Table 2 shows the average log likelihood over each
tract variable across both databases. These re-
sults are consistent with the state-of-the-art (Toda
et al., 2008). In the following experiments, we use
MDNs that produce 4 Gaussians.
65

Acoustics
ASR
ASR
MDN
MDN
W
1
W
2

W
N

N-best
hypotheses
TADA
TADA
TV
1
TV
2

TV
N
Canonical
Tract
Variables
TRANS
TRANS
TV*
1
TV*
2

TV*
N
Modified
Tract
Variables
P(TV
i
*)
W*

1
W*
2

W*
N
Reranked
list
Figure 5: The TD-ASR mechanism for deriving articulatory likelihoods, L
Λ
(W
i
), for each word sequence
W
i
produced by standard acoustic techniques.
Manner Canonical Transformed
approximant 0.19 0.16
fricative 0.37 0.29
nasal* 0.24 0.18
retroflex 0.23 0.19
plosive 0.10 0.08
vowel 0.27 0.25
Table 3: Average difference between predicted
tract variables and observed data, on [0, 1] scale.
(*) Nasals are evaluated only with MOCHA data,
since TORGO data lacks velum measurements.
We evaluate how closely transformations to the
canonical tract variables predicted by TADA match
the data. Namely, we input the known orthography

for each test utterance into TADA, obtain the pre-
dicted canonical tract variables TV, and transform
these according to our trained SKF. The resulting
predicted and transformed sequences are aligned
with our measurements derived from EMA with
dynamic time warping. Finally, we measure the
average difference between the observed data and
the predicted (canonical and transformed) tract
variables. Table 3 shows these differences accord-
ing to the phonological manner of articulation. In
all cases the transformed tract variable motion is
more accurate, and significantly so at the 95% con-
fidence level for nasal and retroflex phonemes, and
at 99% for fricatives. The practical utility of the
transformation component is evaluated in its effect
on recognition rates, as described below.
6.2 Recognition with TD-ASR
With the performance of the components of TD-
ASR better understood, we combine these and
study the resulting composite TD-ASR system.
0 0.2 0.4 0.6 0.8 1
8
8.5
9
9.5
10
α
WER (%)

TORGO
MOCHA
Figure 6: Word-error-rate according to varying α,
for both TORGO and MOCHA data.
Figure 6 shows the WER as a function of α with
TD-ASR and N = 4 hypotheses per utterance. The
effect of α is clearly non-monotonic, with articula-
tory information clearly proving useful. Although
systems whose rankings are weighted solely by the
articulatory component perform better than the ex-
clusively acoustic systems, the lists available to the
former are procured from standard acoustic ASR.
Interestingly, the gap between systems trained to
the two databases increases as α approaches 1.0.
Although this gap is not significant, it may be the
result of increased inter-speaker articulatory varia-
tion in the TORGO database, which includes more
than twice as many speakers as MOCHA.
Figure 7 shows the WER obtained with TD-
ASR given varying-length N-best lists and α =
0.7. TD-ASR accuracy at N = 4 is significantly
better than both TD-ASR at N = 2 and the base-
line approaches of table 1 at the 95% confidence
level. However, for N > 4 there is a noticeable
and systematic worsening of performance.
66
2 3 4 5 6 7 8
8.2
8.4
8.6

8.8
9
9.2
9.4
9.6
9.8
Length of N−best list
WER (%)


TORGO
MOCHA
Figure 7: Word-error-rate according to vary-
ing lengths of N-best hypotheses used, for both
TORGO and MOCHA data.
The optimal parameterization of the TD-ASR
model results in an average word-error-rate of
8.43%, which represents a 10.3% relative error re-
duction over the best parameterization of our base-
line models. The SKF model of section 4 differs
from the HMM and DBN-A baseline models only
in its use of continuous (rather than discrete) hid-
den dynamics and in its articulatory observations.
However, its performance is far more variable, and
less conclusive. On the MOCHA database the
SKF model had an average of 9.54% WER with
a standard deviation of 0.73 over 5 trials, and an
average of 9.04% WER with a standard deviation
of 0.64 over 5 trials on the TORGO database. De-
spite the presupposed utility of direct articulatory

observations, the SKF system does not perform
significantly better than the best DBN-A model.
Finally, the experiments of tables 6 and 7 are
repeated with the canonical tract variables passed
untransformed to the probability maps generated
by the MDNs. Predictably, resulting articulatory
likelihoods L
Λ
are less representative and increas-
ing their contribution α to the hypothesis rerank-
ing does not improve TD-ASR performance sig-
nificantly, and in some instances worsens it. Al-
though TADA is a useful prescriptive model of
generic articulation, its use must be tempered with
knowledge of inter-speaker variability.
7 Discussion and conclusions
The articulatory medium of speech rarely informs
modern speech recognition. We have demon-
strated that the use of direct articulatory knowl-
edge can substantially reduce phoneme and word
errors in speech recognition, especially if that
knowledge is motivated by high-level abstrac-
tions of vocal tract behaviour. Task dynamic the-
ory provides a coherent and biologically plausible
model of speech production with consequences for
phonology (Browman and Goldstein, 1986), neu-
rolinguistics (Guenther and Perkell, 2004), and the
evolution of speech and language (Goldstein et al.,
2006). We have shown that it is also useful within
speech recognition.

We have overcome a conceptual impediment in
integrating task dynamics and ASR, which is the
former’s deterministic nature. This integration is
accomplished by stochastically transforming pre-
dicted articulatory dynamics and by calculating
the likelihoods of these dynamics according to
speaker data. However, there are several new av-
enues for exploration. For example, task dynamics
lends itself to more general applications of con-
trol theory, including automated self-correction,
rhythm, co-ordination, and segmentation (Fried-
land, 2005). Other high-level questions also re-
main, such as whether discrete gestures are the
correct biological and practical paradigm, whether
a purely continuous representation would be more
appropriate, and whether this approach general-
izes to other languages.
In general, our experiments have revealed very
little difference between the use of MOCHA and
TORGO EMA data. An ad hoc analysis of some
of the errors produced by the TD-ASR system
found no particular difference between how sys-
tems trained to each of these databases recognized
nasal phonemes, although only those trained with
MOCHA considered velum motion. Other errors
common to both sources of data include phoneme
insertion errors, normally vowels, which appear to
co-occur with some spurious motion of the tongue
between segments, especially for longer N-best
lists. Despite the relative slow motion of the ar-

ticulators relative to acoustics, there remains some
intermittent noise.
As more articulatory data becomes available
and as theories of speech production become more
refined, we expect that their combined value to
speech recognition will become indispensable.
Acknowledgments
This research is funded by the Natural Sciences
and Engineering Research Council and the Uni-
versity of Toronto.
67
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